ArticleScientific reports2024
Concurrent validity and test reliability of the deep learning markerless motion capture system during the overhead squat.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Comparative Analysis of Markerless Motion-Capture Models for Assessing Football Kinematics During 30 m Long-Pass Tasks.Sensors (Basel, Switzerland) · 2026Article
- Evaluating a Multi-Camera Markerless System for Capturing Basketball-Specific Movements: An Exploration Using 25 Hz Video Streams.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence in postural management: a critical review of detection, correction, and clinical applicability.Journal of orthopaedic surgery and research · 2026Review
- Multimodal sequence dynamics and convergence optimization in dual-stream LSTM networks for complex physiological state estimation.Frontiers in neurorobotics · 2026Article
- The relative age effect among Malaysian university athletes: a cross-sectional survivorship analysis with exploratory AI-based movement screening.Frontiers in sports and active living · 2026Article
- Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.eNeuro · 2025Article
- Between-Day Reliability of Kinematic Variables Using Markerless Motion Capture for Single-Leg Squat and Single-Leg Landing Tasks.International journal of sports physical therapy · 2025Article
- Combining video telemetry and wearable MEG for naturalistic imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Decade-long insights into AI for orthopedic rehabilitation mapping research networks and future trajectories.Digital healthArticle
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Authors and funding
6 authors.
Funding
Abstract
Marker-based optical motion capture systems have been used as a cardinal vehicle to probe and understand the underpinning mechanism of human posture and movement, but it is time-consuming for complex and delicate data acquisition and analysis, labor-intensive with highly trained operators. To mitigate such inherent issues, we developed an accurate and usable (5-min data collection and processing) deep-learning-based 3-Dimensional markerless motion capture system called "Ergo", designed for use in ecological digital healthcare environments. We investigated the concurrent validity and the test-retest reliability of the Ergo system measurement's whole body joint kinematics (time series joint angles and peak joint angles) data by comparing it with a standard marker-based motion capture system recorded during an overhead squat movement. The Ergo system demonstrated excellent agreement for time series joint angles (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.